The proposed framework is evaluated on two different tissue types for two different tasks in order to demonstrate its capabilities. Our colorectal cancer dataset [1]} is comprised of visual fields (refer as images for simplicity) extracted from colorectal histology images based on a two-tier [2]}, [3]} grading system. The CRC dataset consists of 139 images with an average size of \(4,548\times 7,520\)  pixels obtained at \(20\times \)  magnification. These images are classified into three different classes (normal, low grade, and high grade) based on the organization of glands in the images by the expert pathologist. We follow 3-fold cross validation for a fair comparison of the proposed method with the method presented in [1]}. We extracted patches of two different sizes for the training of traditional patch classifiers (\(224\times 224\)  pixels) and our proposed framework (\(1,792\times 1,792\)  pixels). A detailed distribution of the patches is presented in table REF . We introduced background class to handle the patches with no or little glandular regions.
In breast cancer classification [5]}, our goal is to classify the images into four different tissue sub-types namely normal, benign, in-situ, and invasive. We use exactly the same training, validation, and test splits as in [6]} for a fair comparison of our proposed framework with the existing context-aware methods.
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